Information, strategic behavior, and fairness in ultimatum bargaining: an experimental study
Journal of Mathematical Psychology - Special issue on experimental economics
A statistical analysis of the trading agent competition 2001
ACM SIGecom Exchanges
The Influence of Social Dependencies on Decision-Making: Initial Investigations with a New Game
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
The First International Trading Agent Competition: Autonomous Bidding Agents
Electronic Commerce Research
A Real Trading Model based Price Negotiation Agents
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
Modeling agents through bounded rationality theories
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ACM SIGSOFT Software Engineering Notes
Static and expanding grid coverage with ant robots: Complexity results
Theoretical Computer Science
Multi-agent Cooperative Cleaning of Expanding Domains
International Journal of Robotics Research
Using aspiration adaptation theory to improve learning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Modeling agents based on aspiration adaptation theory
Autonomous Agents and Multi-Agent Systems
KEMNAD: A Knowledge Engineering Methodology For Negotiating Agent Development
Computational Intelligence
On the failure of game theoretic approach for distributed deadlock resolution
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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As computerized agents are becoming more and more common, e-commerce becomes a major candidate for incorporation of automated agents. Thus, it is vital to understand how people design agents for online markets and how their design changes over time. This, in turn, will enable better design of agents for these environments. We focus on the design of trading agents for bilateral negotiations with unenforceable agreements. In order to simulate this environment we conducted an experiment with human subjects who were asked to design agents for a resource allocation game. The subjects' agents participated in several tournaments against each other and were given the opportunity to improve their agents based on their performance in previous tournaments. Our results show that, indeed, most subjects modified their agents' strategic behavior with the prospect of improving the performance of their agents, yet their average score significantly decreased throughout the tournaments and became closer to the equilibrium agents' score. In particular, the subjects modified their agents to break more agreements throughout the tournaments. In addition, the subjects increased their means of protection against deceiving agents.